Supervised machine learning in wind forecasting and ramp event prediction /

"[A]n up to date overview of the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin suppo...

Full description

Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Dhiman, Harsh S. (Author), Deb, Dipankar (Author), Balas, Valentina Emilia (Author)
Format: Electronic eBook
Language:Inglés
Published: London, United Kingdom : Academic Press, 2020.
Series:Wind energy engineering series.
Subjects:
Wind forecasting > Data processing.
Machine learning.
Vents > Pr�evision > Informatique.
Apprentissage automatique.
Machine learning
Online Access:Texto completo
Table of Contents:
  • Introduction: Renewable energy focus
  • Wind energy : issues and challenges
  • Machine learning in allied areas of wind energy
  • Scope and outline of the book
  • Wind energy fundamentals: Basics of wind power
  • Wind resource assessment
  • Wind turbine micrositing
  • Paradigms in wind forecasting: Introduction to time series
  • Wind forecasting overview
  • Statistical methods
  • Machine learning-based models
  • Hybrid wind forecasting methods
  • Supervised machine learning models based on support vector regression: Support vector regression
  • ₀-support vector regression
  • Least-square support vector regression
  • Twin support vector regression
  • ₀-twin support vector regression
  • Decision tree ensemble-based regression models: Random forest regression
  • Gradient boosted machines
  • Hybrid machine intelligent wind speed forecasting models: Introduction
  • Wavelet transform
  • Framework of hybrid forecasting
  • Results and discussion
  • Empirical mode of decomposition-based SVR variants for wind speed prediction
  • Ramp prediction in wind farms: Ramp events in scientific and engineering activities
  • Ramp events in wind farms
  • Ramp event analysis for onshore and offshore wind farms
  • Supervised learning for forecasting in presence of wind wakes: Introduction
  • Wind wakes
  • Wake effect in wind forecasting
  • Results
  • Epilogue
  • Introduction to R for machine learning regression: Data handling in R
  • Linear regression analysis in R
  • Support vector regression in R
  • Random forest regression in R
  • Gradient boosted machines in R.

Similar Items